Common Use SQL

Structured Query Language (SQL) is a computer language that helps you interact with databases. Here you can find common use SQL statements that you can use when querying the data.

Query 1: Aggregate Store Sales

This 'SELECT' statement will aggregate the Store Sales data and show the respective sums between Walmart Year Week Number 202340 and 202400 by wm_item_nbr, svc_chnl_nm, wm_yr_wk_nbr, vendor_nbr, vendor_nm, store_nbr.

SELECT wm_item_nbr, svc_chnl_nm, wm_yr_wk_nbr, vendor_nbr, vendor_nm, store_nbr, sum(ty_sales_amt) as ty_sales_amt, sum(ly_sales_amt) as ly_sales_amt, sum(ty_qty) as ty_qty, sum(ly_qty) as ly_qty, sum(ty_aur) as ty_aur, sum(ly_aur) as ly_aur, sum(ly_scan_cnt) as ly_scan_cnt, sum(ty_scan_cnt) as ty_scan_cnt
from wmt-dv-bi-link-prod.dv_supplier.store_sales
where wm_yr_wk_nbr between 202340 and 202400
group by wm_item_nbr, svc_chnl_nm, wm_yr_wk_nbr, vendor_nbr, vendor_nm, store_nbr, vendor_nm;

Query 2: Join Store Item Measures, Store Sales, and Store Dimensions

This query joins Store Item Measures, Store Sales, and Store Dimensions data to get a more comprehensive view of store metrics between 202350 and 202400.

SELECT sum(ty_in_trnst_qty) as ty_in_trnst_qty, sum(ty_in_trnst_cost_amt) as ty_in_trnst_cost_amt, sum(ty_in_whse_qty) as ty_in_whse_qty, storeitem.wm_item_nbr
from wmt-dv-bi-link-prod.dv_supplier.store_invt storeitem
join wmt-dv-bi-link-prod.dv_supplier.store_sales storesales
on storeitem.bus_dt = storesales.bus_dt and storeitem.store_nbr = storesales.store_nbr and storeitem.wm_item_nbr = storesales.wm_item_nbr
join wmt-dv-bi-link-prod.dv_supplier.store_dim storedim on storedim.store_nbr = storeitem.store_nbr
where storesales.wm_yr_wk_nbr between 202350 and 202400
group by storeitem.wm_item_nbr

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